Cong S. Zong

2.2k citations
24 papers · 1.3k indexed · h-index 17
Topics
PI3K/AKT/mTOR signaling in cancer (8 papers)Growth Hormone and Insulin-like Growth Factors (6 papers)Cytokine Signaling Pathways and Interactions (6 papers)
Partner nations
United StatesChina

In The Last Decade

Cong S. Zong

24 papers receiving 1.3k citations

Peers

Cong S. Zong
Comparison fields: 5 of 89
  • Molecular Biology 825
  • Oncology 495
  • Endocrinology, Diabetes and Metabolism 205
  • Immunology 200
  • Cancer Research 168
Replace Ling-Mei Wang with:
Ling-Mei Wang United States
Giulia Colletta Italy
Evelyne Goillot France
Paolo Salerno Italy
Marco Arndt Germany
Valentina Evdokimova Canada
Akira Inomata Japan
Aleata A. Triplett United States
Brian C. Grabiner United States
Prabakaran Kesavan United States
Cong S. Zong relative to Ling-Mei Wang United States Ling-Mei Wang's profile →
Citations per field
00.5×1.5×
Ling-Mei Wang · 1×
Citations per year

Countries citing papers authored by Cong S. Zong

Since Specialization
Citations

This map shows the geographic impact of Cong S. Zong's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Cong S. Zong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cong S. Zong more than expected).

Fields of papers citing papers by Cong S. Zong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cong S. Zong. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Cong S. Zong. The network helps show where Cong S. Zong may publish in the future.

Co-authorship network of co-authors of Cong S. Zong

This figure shows the co-authorship network connecting the top 25 collaborators of Cong S. Zong. A scholar is included among the top collaborators of Cong S. Zong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Cong S. Zong. Cong S. Zong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 51
2 172
3 98
4 16
5 164
6 46
7 54
8
Inhibition of mitogen-activated protein kinase kinase selectively inhibits cell proliferation in human breast cancer cells displaying enhanced insulin-like growth factor I-mediated mitogen-activated protein kinase activation.
40
9 203
10 81
11 26
12 25
13 2
14 33
15 68
16 11
17 23
18 6
19 6
20 7

About Cong S. Zong

Cong S. Zong is a scholar working on Oncology, Endocrinology, Diabetes and Metabolism and Molecular Biology, having authored 24 papers that have together received 1.3k indexed citations. Recurring topics across this work include PI3K/AKT/mTOR signaling in cancer (8 papers), Growth Hormone and Insulin-like Growth Factors (6 papers) and Cytokine Signaling Pathways and Interactions (6 papers). The work is most often cited by research in Oncology (495 citations), Endocrinology, Diabetes and Metabolism (205 citations) and Molecular Biology (825 citations). Cong S. Zong has collaborated with scholars based in United States and China. Frequent co-authors include Lu‐Hai Wang, Ulrich Hermanto, Joseph L.‐K. Chan, Henry B. Sadowski, Weiqun Li, Curt M. Horvath, David T. Levy, Yixing Jiang, Weizhou Zhang and Mien‐Chie Hung. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and The Journal of Experimental Medicine.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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